Jeong, Ji-Hyeok Choi, Jun-Hyuk Kim, Keun-Tae Lee, Song-Joo Kim, Dong-Joo Kim, Hyung-Min 2024-01-19T13:33:34Z 2024-01-19T13:33:34Z 2022-01-10 2021-10 1424-8220 https://pubs.kist.re.kr/handle/201004/116311 Motor imagery (MI) brain-computer interfaces (BCIs) have been used for a wide variety of applications due to their intuitive matching between the user&apos;s intentions and the performance of tasks. Applying dry electroencephalography (EEG) electrodes to MI BCI applications can resolve many constraints and achieve practicality. In this study, we propose a multi-domain convolutional neural networks (MD-CNN) model that learns subject-specific and electrode-dependent EEG features using a multi-domain structure to improve the classification accuracy of dry electrode MI BCIs. The proposed MD-CNN model is composed of learning layers for three domain representations (time, spatial, and phase). We first evaluated the proposed MD-CNN model using a public dataset to confirm 78.96% classification accuracy for multi-class classification (chance level accuracy: 30%). After that, 10 healthy subjects participated and performed three classes of MI tasks related to lower-limb movement (gait, sitting down, and resting) over two sessions (dry and wet electrodes). Consequently, the proposed MD-CNN model achieved the highest classification accuracy (dry: 58.44%; wet: 58.66%; chance level accuracy: 43.33%) with a three-class classifier and the lowest difference in accuracy between the two electrode types (0.22%, d = 0.0292) compared with the conventional classifiers (FBCSP, EEGNet, ShallowConvNet, and DeepConvNet) that used only a single domain. We expect that the proposed MD-CNN model could be applied for developing robust MI BCI systems with dry electrodes.</p> English Multidisciplinary Digital Publishing Institute (MDPI) Multi-Domain Convolutional Neural Networks for Lower-Limb Motor Imagery Using Dry vs. Wet Electrodes Article 10.3390/s21196672 1 Sensors, v.21, no.19 Sensors 21 19 Y scie scopus 000709535800001 2-s2.0-85116433003 Chemistry, Analytical Engineering, Electrical & Electronic Instruments & Instrumentation Chemistry Engineering Instruments & Instrumentation Article BRAIN-COMPUTER INTERFACE EEG brain-computer interfaces electroencephalography motor imagery lower limb electrodes neural networks multilayer neural network